# cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science

**Authors:** Harun Bayraktar, Ali Charara, David Clark, Saul Cohen, Timothy Costa, Yao-Lung L. Fang, Yang Gao, Jack Guan, John Gunnels, Azzam Haidar, Andreas Hehn, Markus Hohnerbach, Matthew Jones, Tom Lubowe, Dmitry Lyakh, Shinya Morino, Paul Springer, Sam Stanwyck, Igor Terentyev, Satya Varadhan  
**arXiv:** [2308.01999](https://arxiv.org/abs/2308.01999)  
**Date:** 2023-08-03  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2308.01999v1/

## Key points

- NVIDIA cuQuantum SDK is a library for GPU-accelerated quantum circuit simulations
- It offers composable primitives for quantum circuit simulations
- Classical simulation of quantum devices has become challenging, making fast and scalable simulators crucial
- cuQuantum SDK accelerates and scales up quantum circuit simulators developed by the quantum information science community
- It provides efficient scalable software building blocks optimized for NVIDIA GPU platforms
- Supports state vector and tensor network-based simulators, including approximate tensor network simulation methods
- Quantum circuit simulators integrated with cuQuantum SDK show significant acceleration compared to CPU-only execution
- Enables seamless transition to distributed GPU-accelerated platforms like cloud service providers or supercomputing centers
- Accessible through Python and C APIs, catering to different programming languages
- Empowers researchers and developers in accelerating their quantum circuit simulations

## Summary

The NVIDIA cuQuantum SDK is a cutting-edge library that offers a range of composable primitives for GPU-accelerated quantum circuit simulations. With the increasing size of quantum devices, classical simulation has become more challenging, making fast and scalable quantum circuit simulators crucial for both quantum algorithm developers and quantum hardware engineers focused on device validation and optimization. The cuQuantum SDK was specifically designed to accelerate and scale up quantum circuit simulators developed by the quantum information science community. It enables them to leverage efficient scalable software building blocks optimized for NVIDIA GPU platforms. These functional building blocks cater to the needs of state vector- and tensor network-based simulators, including approximate tensor network simulation methods based on matrix product state, projected entangled pair state, and other factorized tensor representations. By harnessing the immense computing power of the latest NVIDIA GPU architectures, quantum circuit simulators integrated with the cuQuantum SDK demonstrate significant acceleration compared to CPU-only execution for both state vector and tensor network simulation methods. Moreover, by utilizing the parallel primitives available in the cuQuantum SDK, it becomes seamless to transition to distributed GPU-accelerated platforms such as those provided by cloud service providers or high-performance computing systems deployed by supercomputing centers. This extension allows for scaling up possible quantum circuit simulations. The rich capabilities offered by the cuQuantum SDK are conveniently accessible through Python and C application programming interfaces (APIs). The Python API caters directly to a broad Python quantum community while the C API facilitates tight integration with simulators written in any programming language. In summary, the NVIDIA cuQuantum SDK provides a high-performance library that empowers researchers and developers in accelerating their quantum circuit simulations. By leveraging powerful GPUs and offering scalable software building blocks, it enables faster and more efficient simulations for both state vector- and tensor network-based approaches. The flexibility of its APIs ensures compatibility with different programming languages while facilitating seamless transitions to distributed GPU-accelerated platforms.